Comparer des méthodes

	Test du Khi-deux d'indépendance	Kappa de Fleiss pour l'accord inter-juges multiple	Test de McNemar
Domaine	Statistique	Statistique	Statistique
Famille	Hypothesis test	Hypothesis test	Hypothesis test
Année d'origine	1900	1971	1947
Auteur d'origine	Karl Pearson	Joseph L. Fleiss	Quinn McNemar
Type	Nonparametric test of association	Non-parametric agreement measure	Nonparametric test for paired binary data
Source fondatrice	Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50(302), 157–175.	Fleiss, J.L. (1971). Measuring Nominal Scale Agreement Among Many Raters. Psychological Bulletin, 76(5), 378–382.	McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2), 153–157.
Alias	chi-squared test, Pearson's chi-square test, test of independence, ki-kare bağımsızlık testi	multi-rater kappa, Fleiss kappa, Fleiss' Kappa (Çoklu Değerlendirici Uyumu)	McNemar chi-square test, test for correlated proportions, paired binary test, McNemar Testi
Apparentées	2	2	5
Résumé	The chi-square test of independence is a nonparametric hypothesis test that examines whether two categorical variables are associated by comparing observed and expected frequencies in a cross-tabulation. It rests on the chi-square criterion introduced by Karl Pearson in 1900.	Fleiss' Kappa is a non-parametric statistic for measuring the degree of agreement among three or more raters who classify items into mutually exclusive nominal categories. Introduced by Joseph L. Fleiss in 1971 as a generalization of Cohen's Kappa beyond two raters, it corrects observed agreement for the level of agreement expected by chance alone, making it the standard reliability index in medical diagnosis studies, content analysis, and multi-coder research.	McNemar's test is a nonparametric hypothesis test that compares two paired (correlated) binary proportions, such as a yes/no measurement taken on the same subjects before and after an intervention. It was introduced by Quinn McNemar in 1947 and works on the 2×2 table of matched outcomes.